Maciej Beręsewicz
24 May 2015
Podstawowe zmiany:
gosp %>%
ggvis(x = ~wydg)
gosp %>%
ggvis(x = ~log(wydg))
gosp %>%
ggvis(x = ~klm)
gosp %>%
ggvis(x = ~as.factor(klm)) %>%
scale_ordinal('x')
gosp %>% ggvis(~dochg, ~wydg) %>% layer_points()
gosp %>% ggvis(~dochg, ~wydg) %>% layer_points() %>%
layer_model_predictions(model = "lm", se = TRUE)
gosp %>% mutate(klm=as.factor(klm)) %>% group_by(klm) %>%
ggvis(~dochg, ~wydg, fill=~klm) %>% layer_points() %>%
layer_model_predictions(model = "lm", se = TRUE)
gosp %>% mutate(klm=as.factor(klm)) %>% group_by(klm) %>%
ggvis(~dochg, ~wydg, stroke =~klm, fill= ~ klm) %>% layer_points() %>%
layer_model_predictions(model = "lm", se = TRUE)
gosp %>% na.omit() %>%
ggvis(x=~as.factor(klm), y=~wydg) %>% layer_boxplots()
gosp %>%
slice(1:100) %>%
ggvis(~dochg, ~wydg) %>%
layer_points() %>%
add_tooltip( function(x) {
print('Dochód:', x$dochg, '\nWydatki:', x$wydg)
}, 'hover')
gosp %>%
slice(1:1000) %>%
ggvis(~dochg, ~wydg) %>%
layer_points(size := input_slider(1, 50, step = 5)) %>%
layer_smooths(span = input_slider(0.5, 2, step = 0.1))
gosp %>%
slice(1:1000) %>%
ggvis(~dochg, ~wydg) %>%
layer_points(opacity := input_slider(0, 1, step = 0.1))
gosp %>%
ggvis(x= ~wydg) %>%
layer_histograms(width = input_slider(1, 100, step = 10))
gosp %>%
select(wydg) %>% na.omit() %>%
ggvis(x = ~wydg) %>%
layer_densities(
adjust = input_slider(.1, 2, value = 1, step = .1, label = "Bandwidth adjustment"),
kernel = input_select(
c("Gaussian" = "gaussian",
"Epanechnikov" = "epanechnikov",
"Rectangular" = "rectangular",
"Triangular" = "triangular",
"Biweight" = "biweight",
"Cosine" = "cosine",
"Optcosine" = "optcosine"),
label = "Kernel")
)
Możemy zmieniać:
i wiele innych
gosp %>%
ggvis(x= ~dochg,y = ~wydg) %>%
add_axis('x', title='Dochody') %>%
add_axis('y', title = 'Wydatki')
Wspierane typy osi: